Application of optimal RBF neural networks for optimization and characterization of porous materials

Optimization and characterization of porous materials have been extensively studied by various surface phenomena researchers. Efficient methods are required to predict the optimum values of operating parameters in different stages of material preparation and characterization processes. A novel method based on the application of a special class of radial basis function neural network known as Regularization network is presented in the this article. A reliable procedure is introduced for efficient training of the optimal isotropic Gaussian Regularization network using experimental data sets. Two different practical case studies on optimization and characterization of carbon molecular sieves and activated c I e ©